Gaining an accurate view of revenue intelligence for B2B markers is challenging. With disconnected and dirty data residing in many systems, customers need a solution that collects, normalizes, and aggregates information into reports that answer the questions B2B marketers should have a handle on. And let’s face it, no matter how great a set of standard reports might be, every customer wants to see their data a little differently.
Data infrastructure is rapidly growing and evolving along with infrastructure for AI/ML, with the latter growing largely independent from the former. An emerging generation of AI/ML tooling emphasizes data-centric versus model-centric approaches to the ML development lifecycle. These tools recognize that data is the foundation for AI and seek to open opportunities for all data professionals to participate by eliminating the unnecessary complexity of traditional model-centric solutions.
